A ModernBERT-base model continually pretrained on tourism domain data in Spanish and Valencian. This specialized encoder model is optimized for understanding tourism-related texts, including hotel descriptions, destination guides, travel services, and cultural heritage content.

Model Description
Training Data
Training Configuration
Training Results
Intended Uses

How to Use
Additional Information

Model Description

Attribute	Value
Base Model	BSC-LT/MrBERT
Architecture	FlexBERT (22 layers, 768 hidden, 12 heads)
Parameters	~149M
Vocabulary Size	256,000 tokens
Max Sequence Length	8,192 tokens
Languages	Spanish (es), Valencian (va)
Domain	Tourism

Training Data

This model was trained on the gplsi/alia_tourism dataset, filtered for Spanish and Valencian languages.

Dataset Statistics

Metric	Value
Total Documents	66,548
Spanish Documents	49,644 (74.6%)
Valencian Documents	16,904 (25.4%)
Raw Text Size	1.2 GB
Training Samples	80,839
Validation Samples	8,862
Total Tokens (Train)	~348 million
Tokens Seen (4 epochs)	~1.39 billion

Data Processing Pipeline

Download: Extracted from gplsi/alia_tourism HuggingFace dataset
Filtering: Selected only language=["es", "va"] subsets
Tokenization: BPE tokenization with MrBERT tokenizer (256k vocab)
Chunking: Packed into 8,192-token sequences
Split: 90% train / 10% validation

Training Configuration

Parameter	Value
Training Epochs	4
Sequence Length	8,192
MLM Probability	30% (train), 15% (eval)
Batch Size	32
Learning Rate	5e-5 (cosine decay to 5e-6)
Warmup	101 batches (1%)
Optimizer	StableAdamW
Precision	bfloat16
Hardware	1× NVIDIA RTX 4090

Training Results

Epoch	Training Loss	Masked Accuracy
1	2.84 → 1.30	80.64% → 84.39%
2	1.07 → 1.05	85.67%
3	0.92 → 1.26	86.11%
Final	1.26	86.11%

Key Achievements

✅ 87% loss reduction (9.4 → 1.26)
✅ +5.5 pp accuracy gain (80.6% → 86.1%)
✅ No overfitting observed
✅ Stable gradients throughout training

Intended Uses

Primary Use Cases

Tourism NLP: Named entity recognition, text classification, sentiment analysis for tourism content
Semantic Search: Document retrieval and similarity for travel-related queries
Information Extraction: Extracting entities like hotels, destinations, amenities
Multilingual Tourism: Processing Spanish and Valencian tourism texts

Out-of-Scope Uses

General-purpose language understanding outside tourism domain
Languages other than Spanish and Valencian
Text generation (this is an encoder-only model)

Limitations

Domain-specific: Performance may degrade on non-tourism texts
Language coverage: Optimized for Spanish (es) and Valencian (va) only
Encoder-only: Cannot generate text, only encode/understand

Ethical Considerations

The training data is automatically curated from tourism sources and may contain:

Geographic and cultural biases toward specific regions
Commercial content from tourism businesses
Limited representation of certain destinations or services

Users should evaluate the model's outputs for fairness and bias in their specific applications.

How to Use

Transformers

python

from transformers import AutoModelForMaskedLM, AutoTokenizer

model = AutoModelForMaskedLM.from_pretrained("gplsi/Aitana-tourism-mb-encoder-1.0")
tokenizer = AutoTokenizer.from_pretrained("gplsi/Aitana-tourism-mb-encoder-1.0")

# Fill-mask example
text = "El hotel ofrece vistas [MASK] al mar Mediterráneo."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)

# Get predictions
import torch
mask_token_index = (inputs.input_ids == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
predicted_token_id = outputs.logits[0, mask_token_index].argmax(axis=-1)
print(tokenizer.decode(predicted_token_id))

For Embeddings

python

from transformers import AutoModel, AutoTokenizer
import torch

model = AutoModel.from_pretrained("gplsi/Aitana-tourism-mb-encoder-1.0")
tokenizer = AutoTokenizer.from_pretrained("gplsi/Aitana-tourism-mb-encoder-1.0")

text = "Descubre las playas de la Costa Blanca"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)

with torch.no_grad():
    outputs = model(**inputs)
    embeddings = outputs.last_hidden_state.mean(dim=1)  # Mean pooling

Evaluation

This model was evaluated using the GLUE and SuperGLUE benchmarks.

Suite	Task/Metric	Seeds	Scores by seed	Mean +/- std
GLUE	CoLA MCC	4	19: 7.05, 8364: 8.50, 717: 12.39, 10536: 11.77	9.93 +/- 2.57
GLUE	MNLI acc	1	19: 65.26	65.26
GLUE	MNLI-mm acc	1	19: 65.69	65.69
GLUE	MRPC F1	5	19: 74.46, 8364: 73.59, 717: 70.83, 10536: 73.16, 90166: 73.74	73.16 +/- 1.38
GLUE	QNLI acc	1	19: 63.44	63.44
GLUE	QQP F1	1	19: 78.97	78.97
GLUE	RTE acc	5	19: 49.10, 8364: 48.38, 717: 52.71, 10536: 53.43, 90166: 50.18	50.76 +/- 2.22
GLUE	SST-2 acc	3	19: 80.39, 8364: 80.28, 717: 80.73	80.47 +/- 0.24
GLUE	STS-B Spearman	5	19: 29.77, 8364: 24.73, 717: 28.84, 10536: 25.05, 90166: 29.55	27.59 +/- 2.49
SuperGLUE partial	MNLI acc	1	19: 66.39	66.39
SuperGLUE partial	MNLI-mm acc	1	19: 67.48	67.48
SuperGLUE partial	RTE acc	5	19: 52.35, 8364: 48.01, 717: 53.79, 10536: 54.87, 90166: 50.54	51.91 +/- 2.72
SuperGLUE partial	BoolQ acc	3	23: 64.80, 42: 64.68, 6033: 65.75	65.08 +/- 0.59
SuperGLUE partial	CB acc	3	23: 71.43, 42: 71.43, 6033: 69.64	70.83 +/- 1.03
SuperGLUE partial	CB F1	3	23: 59.05, 42: 49.89, 6033: 58.92	55.95 +/- 5.25
SuperGLUE partial	COPA acc	5	23: 54.00, 42: 48.00, 6033: 47.00, 1337: 53.00, 24: 49.00	50.20 +/- 3.11
SuperGLUE partial	SWAG acc	1	19: 27.55	27.55
SuperGLUE partial	WiC acc	3	23: 57.21, 42: 57.84, 6033: 57.37	57.47 +/- 0.33

Additional Information

Author

The model has been developed by the Language and Information Systems Group (GPLSI) and the Centro de Inteligencia Digital (CENID), both part of the University of Alicante (UA), as part of their ongoing research in Natural Language Processing (NLP).

Funding

This work is funded by the Ministerio para la Transformación Digital y de la Función Pública, co-financed by the EU – NextGenerationEU, within the framework of the project Desarrollo de Modelos ALIA.

Acknowledgments

We would like to express our gratitude to all individuals and institutions that have contributed to the development of this work.

Special thanks to:

We also acknowledge the financial, technical, and scientific support of the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the project Desarrollo de Modelos ALIA, whose contribution has been essential to the completion of this research.

License

This model is released under the Apache License 2.0.

Disclaimer

This model is intended for general purposes and is available under a permissive Apache License 2.0. Be aware that the model may have biases and/or undesirable outputs. Users deploying systems based on this model are responsible for mitigating risks and complying with applicable AI regulations.

Reference

If you use this model, please cite:

bibtex

@misc{modernbert-tourism-2025,
  author = {Yáñez-Romero, Fabio and Sepúlveda-Torres, Robiert and Estevanell-Valladares, Ernesto L. and Galeano, Santiago and Martínez-Murillo, Iván and Grande, Eduardo and Canal-Esteve, Miquel and Miró Maestre, María and Bonora, Mar and Gutierrez, Yoan and Abreu Salas, José Ignacio and Consuegra-Ayala, Juan Pablo and Lloret, Elena and Montoyo, Andrés and Muñoz-Guillena and Palomar, Manuel},
  title = {Aitana Tourism Encoder: Domain-Adapted Language Model for Spanish and Valencian Tourism},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/gplsi/Aitana-tourism-mb-encoder-1.0}}
}

gplsi/Aitana-tourism-mb-encoder-1.0

Aitana-Tourism-Encoder (Spanish & Valencian)

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